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Cleanlab: Labeled Datasets that Correct Themselves Automatically

Posted Jun 30
# Cleanlab
# Labeled Datasets
# Data-centric AI
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SPEAKER
Curtis Northcutt
Curtis Northcutt
Curtis Northcutt
CEO & Co-Founder @ Cleanlab

Curtis Northcutt is the CEO and Co-Founder of Cleanlab focused on making AI work reliably for people and their messy, real-world data by automatically fixing issues in any ML dataset. Curtis completed his Ph.D. in Computer Science at MIT, receiving the MIT Thesis Award, NSF Fellowship, and the Goldwater Scholarship. Prior to Cleanlab, Curtis worked at AI research groups including Google, Oculus, Amazon, Facebook, Microsoft, and NASA.

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Curtis Northcutt is the CEO and Co-Founder of Cleanlab focused on making AI work reliably for people and their messy, real-world data by automatically fixing issues in any ML dataset. Curtis completed his Ph.D. in Computer Science at MIT, receiving the MIT Thesis Award, NSF Fellowship, and the Goldwater Scholarship. Prior to Cleanlab, Curtis worked at AI research groups including Google, Oculus, Amazon, Facebook, Microsoft, and NASA.

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SUMMARY

Pioneered at MIT by 3 Ph.D. Co-Founders, Cleanlab is an open-source/SaaS company building the premier data-centric AI tools workflows for (1) automatically correcting messy data and labels, (2) auto-tracking of dataset quality over time, (3) automatically finding classes to merge and delete, (4) auto ml for data tasks, (5) obtaining and ranking high-quality annotations, and (6) training ML models with messy data.

Most of the prescriptive tasks (finding issues) can be done in one line of code with their open-source product: https://github.com/cleanlab/cleanlab.

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